{"id":"1612a87a-4eff-48ac-a373-ce694c8402a3","arxiv_id":"2507.01334","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"DeepSeek-R1 achieves 75.9% zero-shot and 81.3% few-shot accuracy on filtered SciBench physics problems, far above general-purpose chat models, and correct answers tend to use symbolic derivation.","lead":"This paper tests how well the reasoning-focused AI model DeepSeek-R1 and its smaller variants solve college physics problems from the SciBench benchmark, and compares them with general chat models. It reports that DeepSeek-R1 scores roughly twice as high as GPT-4-Turbo, and that correct solutions often work through equations symbolically before plugging in numbers.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The symbolic-vs-numeric behavioral claim rests on an unvalidated LLM pattern classifier; the two reported categories are non-exclusive and the six-way taxonomy in App. A.5 is collapsed without a stated rule.","rationale":"I read the paper in good faith. The accuracy results in Table 2 are checkable against the public SciBench benchmark and open-weight R1 models, and the deterministic sampling setup gives them some independent footing. The paper's distinctive contribution, however, is the symbolic-versus-numeric reasoning dichotomy, and that claim is only as strong as the classifier that produced Fig. 4. The manuscript itself says the taxonomy was designed through human review and then applied by LLM prompts, but it reports no validation of those prompts, no inter-annotator agreement for the human step, and no sample sizes per model in the pattern figures. The category definitions overlap, the six-to-two collapse is unspecified, and prompting with the reference solution invites label bias. A human-annotation reliability study plus a simple rule-based re-labeling would settle whether the symbolic-correct correlation is real or an artifact. Secondary issues, such as the confusing statement about filtering out detailed-solution problems and the reliance on SciBench-reported baselines for comparison, also need cleanup, but they do not move the verdict beyond the reader's conditional assessment.","tokens_in":14815,"tokens_out":7622,"duration_ms":91910,"concrete_test":"Have two independent annotators label a stratified sample of roughly 100 CoT traces, balanced by correctness and across DeepSeek-R1, both distilled variants, and DeepSeek-V3, using the Fig. 5 taxonomy extended with an explicit 'mixed/both' option. Compute Cohen's kappa for human-human and human-LLM agreement, then recompute the Fig. 4 symbolic-vs-numeric split after excluding or separately counting mixed labels, and compare with a rule-based probe (symbolic iff the first numeric substitution occurs only after a symbolic rearrangement producing a final expression). If kappa is below 0.6, or if the correct-vs-incorrect symbolic shift disappears or reverses under the rule-based probe, the paper's central behavioral claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline accuracy numbers are secondary to its novel behavioral claim: that correct traces from reasoning-specialized models systematically shift toward symbolic derivation. That claim depends entirely on the LLM pattern classifier in Fig. 5, which is never validated. Fig. 5's definitions are not mutually exclusive: the worked 'numeric' solution in Fig. 3 also invokes a symbolic work-energy relation (W = ΔK + ΔEth) before plugging in numbers, and the 'symbolic' solution ends with numeric substitution; a forced single-label choice between them is therefore arbitrary for many traces. App. A.5 defines six categories, yet Fig. 4 reports only two; the paper never states how categories 3–6 or mixed traces were collapsed into the binary symbolic/numeric split. No inter-annotator agreement, per-model sample sizes, or LLM-human agreement is reported. The prompt also supplies the reference solution, so the classifier may label by stylistic similarity to a symbolic-looking reference rather than by the model's actual derivation strategy. Because R1 traces are verbose and equation-heavy by design, the correlation with correctness may be an artifact of label construction. The manuscript itself (A.5) flags human review only as prompt-design input, not as a validation set.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper evaluates DeepSeek-R1 and its distilled variants (R1-distill-LLaMA-70B and R1-distill-Qwen-32B) on three filtered SciBench physics subsets (fundamental physics, thermodynamics, classical dynamics) under both zero-shot and few-shot chain-of-thought prompting. It reports accuracy numbers against SciBench baselines and, using an LLM-based classifier applied to model chain-of-thought traces, categorizes reasoning into symbolic-derivation versus step-by-step numeric-substitution strategies. The paper claims that reasoning-specialized models achieve state-of-the-art accuracy, that few-shot prompting still yields measurable gains, and that reasoning-specialized models systematically shift toward symbolic derivation when producing correct answers.","tokens_in":15006,"tokens_out":6960,"duration_ms":73755,"significance":"If the accuracy results are valid, they provide a useful data point: reasoning-specialized LLMs can solve a substantial fraction of filtered textbook physics problems without external tools, and few-shot prompting continues to help. The reported deterministic inference protocol (temperature 1e-30, n=1, 5% relative tolerance) and the explicit prompting conditions are strengths that would make the accuracy evaluation reproducible in principle. However, the paper's more distinctive behavioral claim—that correct reasoning traces are characterized by symbolic derivation—rests on an unvalidated LLM classifier and a non-exclusive taxonomy, and the baseline comparison is potentially confounded by dataset filtering. The paper does not ship code, data, or classifier outputs, so the behavioral analysis is not reproducible as presented.","major_comments":[{"comment":"The central behavioral claim is not supported by a validated measurement: the paper relies entirely on an LLM-based classifier prompted with the template in Fig. 5, but reports no human-LLM agreement, no inter-annotator reliability, no per-model sample sizes for the percentages in Fig. 4, and no rule for collapsing the six categories defined in App. A.5 into the binary symbolic/numeric split shown in Fig. 4. The prompt also includes the reference solution, which could bias the classifier toward stylistic similarity to a symbolic-looking reference rather than the model's own derivation strategy. As written, the reported correlation between symbolic derivation and correctness cannot be distinguished from an artifact of the labeling procedure.","section":"§4.5, Fig. 4, App. A.5"},{"comment":"The two pattern definitions are not mutually exclusive. The 'Step-by-Step Numeric Substitution' worked example in Fig. 3 itself states the symbolic work-energy relation W = ΔK + ΔEth before substituting numbers, and the 'Symbolic Derivation' example ends with numeric substitution; the real distinction is only the ordering of substitution. For traces that intermix the two styles, a forced single label is arbitrary. The paper gives no decision rule for such borderline cases and no demonstration that the taxonomy is reliable, which undermines the binary distribution in Fig. 4.","section":"§4.5, Fig. 3"},{"comment":"The comparison against baseline models is potentially confounded. The authors filter the SciBench subsets by removing problems containing visual elements and problems that 'require detailed solutions' (§3.2), yet the baseline rows in Table 2 are described simply as 'Data are taken from the SciBench benchmark,' presumably on the unfiltered sets. If the baseline numbers are on the full original SciBench sets while the R1-series numbers are on the filtered subset, the claim that DeepSeek-R1's accuracy is nearly double GPT-4-Turbo's is not a head-to-head comparison. The authors should either re-run the baselines on the exact filtered subsets or verify that the published numbers correspond to those subsets.","section":"§3.2, Table 2"},{"comment":"The paper's own reported data contradict the summary claim. The text states that R1-distill-Qwen-32B 'heavily favors numeric substitution in both incorrect (57.6%) and correct (73.6%) solutions,' meaning its correct solutions are more numeric, not more symbolic. Yet the conclusion asserts that 'reasoning-specialized models systematically shift toward symbolic derivation when producing accurate responses.' This generalization is not supported by the reported distributions and must be either qualified to exclude R1-distill-Qwen-32B or reanalyzed with a more refined claim.","section":"§4.5, Fig. 4"},{"comment":"The error analysis is not reproducible from the manuscript: the figure images are not legible in the provided text (they appear as garbled placeholder characters), the SciBench error categories are not defined in the paper (the prose refers to 'Logical Decomposition,' 'Calculation Skills,' and 'Assumption Identification' without giving their definitions), and the counts cited in the prose (e.g., 'from 7 to 8 errors,' '46 to 37 errors') are not tabulated. The authors should provide a readable figure, a table of error counts, and an explicit mapping from SciBench's ten error categories to the few categories highlighted in the analysis.","section":"§4.4, Figs. 2a/2b"}],"minor_comments":[{"comment":"Several citations do not match the cited claims: Gao et al. (2022) is cited for physics evaluation but the reference is titled 'Evaluating large language models trained on code'; Madaan et al. (2023) is cited for decomposition prompts but the reference is 'Language models can solve computer tasks'; Zheng et al. (2024) is cited for code-generation prompt variants but the reference is 'Prompt engineering for advanced language models.' These references should be corrected.","section":"References"},{"comment":"The sentence 'we filter out problems with detailed solutions to ensure they can be used as few-shot prompts' is contradictory: the paper should clarify whether problems with detailed solutions are used as exemplars or excluded from the evaluation set.","section":"§3.2"},{"comment":"The criterion for selecting the 'top three exemplars from each subset' is not defined. If the exemplars were selected by the authors based on readability or expected performance, the few-shot results may not be representative. The paper should also state whether the exemplars come from the same test set and whether test problems are excluded from the exemplar pool.","section":"§3.4"},{"comment":"The sentence 'The performance advantage is particularly notable given the 32B model's significantly lower computational requirements' appears verbatim twice in this section; one occurrence should be removed.","section":"§4.3"},{"comment":"The token-level metrics 'average token confidence' and 'average token gap' are defined but never analyzed or discussed in the main text, and Figures 6 and 7 are not legible in the provided manuscript. The authors should either present the results with readable figures and interpretation, or remove the appendix.","section":"Appendix B"},{"comment":"The model name is written inconsistently as 'Deepseek' and 'DeepSeek', and there are typographical errors such as 'GPT-4-T URBO' and 'identidy' in App. A.5; these should be corrected in a careful revision.","section":"Throughout"}],"recommendation":"major_revision","confidential_remarks":"The manuscript has the character of an early technical report: several references do not match the cited work, the figures are not rendered correctly in the version I reviewed, and the appendices contain metrics that are never used. More substantively, the central behavioral claim about symbolic derivation would need validation to be published in a serious venue. I recommend major revision rather than rejection because the accuracy evaluation is recoverable and the behavioral claim could be repaired with a validated coding scheme, a clear collapse rule, and a direct baseline comparison on matched subsets."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper gives you two things: new accuracy numbers for DeepSeek-R1 and its distilled variants on three SciBench physics subsets, and a behavioral observation that reasoning models tend to use symbolic derivation in correct solutions. The accuracy numbers are useful and plausible. The behavioral observation is the more interesting part, and it is also the part that needs the most scrutiny.\n\nWhat the paper does well: the evaluation protocol is straightforward, it uses deterministic decoding, and the models are open-weight with a public benchmark, so the headline numbers are checkable. The few-shot improvements are a real finding, and the authors are honest about token costs and the text-only limitation. The accuracy table alone is a reasonable contribution to the benchmark literature.\n\nWhere it gets soft is the reasoning-pattern analysis. The pattern classification is done by an LLM prompted with the template in Figure 5, and that prompt includes the reference solution. The six categories in Appendix A.5 are collapsed into a binary symbolic/numeric split with no stated rule for how categories 3-6 or mixed traces were handled. The categories are not mutually exclusive: the \"numeric\" example in Figure 3 first invokes the work-energy theorem symbolically, and the \"symbolic\" example ends with numeric substitution. There is no validation of the classifier, no inter-annotator agreement, and no per-model sample sizes. The stress-test concern about this is correct. The correlation between symbolic derivation and correctness may be an artifact of the labeling procedure, especially since R1 traces are verbose and equation-heavy by design. The paper's own A.5 describes human review only as prompt-design input, not as a validation step.\n\nLesser issues: the baseline numbers come from the original SciBench paper rather than re-runs under identical conditions, which is acceptable for a comparison but should be explicit. The selection of \"top three\" exemplars is under-specified. The token-level analysis in Appendix B is disconnected from the main claims.\n\nOverall: the accuracy results are solid enough to publish, but the symbolic-reasoning claim is not yet backed by the evidence. This is a paper for readers who track reasoning-LLM performance on science benchmarks, not a methods contribution. It deserves a serious referee more than a desk reject, but the referee should ask for validation of the pattern classifier, a clear aggregation rule for the categories, and ideally larger per-model samples.\n\nRecommendation: send it to review, but with the understanding that the behavioral claim will need real work before it can be accepted.","headline":"Useful, checkable accuracy numbers for DeepSeek-R1 on SciBench physics, but the symbolic-vs-numeric behavioral claim rests on an unvalidated LLM classifier and should be treated as preliminary.","tokens_in":15537,"tokens_out":2002,"would_cite":false,"duration_ms":24651,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Reasoning-focused LLMs reach 75.9% zero-shot accuracy on filtered SciBench physics problems, nearly doubling the best chat-model baseline, and their correct solutions consistently favor symbolic derivation over immediate numeric…","keywords":["large language models","physics reasoning","chain-of-thought prompting","symbolic derivation","SciBench benchmark","DeepSeek-R1","few-shot prompting","error analysis"],"falsifier":"Re-run the pattern analysis on the same 193 problems with two independent human annotators labeling each reasoning trace as symbolic or numeric, and compare their labels with the classifier's; if agreement is near chance, or if rewording the classifier's prompt changes the labels, the reported link between symbolic derivation and correctness would not survive.","tokens_in":14570,"feed_emoji":"⚛️","tokens_out":10129,"duration_ms":104436,"temperature":0.7,"pith_summary":"Deepseek-R1 and its distilled variants can solve most filtered textbook physics problems from the SciBench benchmark without external tools or heavy prompt engineering, reaching 75.9% average accuracy zero-shot and 81.3% with three few-shot exemplars. This is nearly double the best chat-model baseline reported in the benchmark, GPT-4-Turbo at 43.9%. The paper identifies the behavioral difference behind that gap: reasoning-specialized models tend to derive equations symbolically and substitute numbers only at the end, while chat-oriented models plug in numbers step by step, and the symbolic style appears in correct solutions far more often than in wrong ones. It also finds that few-shot prompting still buys measurable gains, especially in classical dynamics, and that a smaller distilled model can beat a larger one on several physics domains.","feed_headline":"Reasoning LLMs hit 75.9% on physics, nearly double GPT-4-Turbo","feed_subtitle":"Symbolic derivation, not step-by-step number crunching, matches correct physics answers; few-shot examples still help.","key_machinery":"The unit of analysis is the chain-of-thought trace, which the authors collect for every model output and then classify by resubmitting it to an LLM with a fixed prompt template listing six reasoning patterns: problem restatement and known-quantity definition, formula selection and symbolic derivation, step-by-step numeric substitution, multi-path or case enumeration, forward versus backward reasoning, and self-check and validation. The load-bearing distinction is between formula selection with symbolic derivation and step-by-step numeric substitution, because the paper's accuracy explanation is built on correct traces favoring the former. Accuracy itself is scored by comparing the parsed numeric answer to the reference under a 5% relative tolerance, using SciBench's error-categorization framework for the error analyses.","core_discovery":"The paper's central claim is that instruction-tuned reasoning models, specifically Deepseek-R1 and its distilled variants, outperform chat-oriented models on physics calculation tasks because they shift toward symbolic derivation: they first write the governing equation in variables, rearrange it into a general expression, and only then substitute the numerical values. On the 193 filtered problems across fundamental physics, thermodynamics, and classical dynamics, this style is strongly associated with correct answers, whereas chat-oriented Deepseek-V3 sticks to step-by-step numeric substitution in both correct and incorrect traces. The paper treats symbolic derivation not as a stylistic side effect but as a substantive contributor to accuracy, and notes one exception, R1-distill-Qwen-32B, which favors numeric substitution yet still performs well, indicating that the style is not the only route to success.","pith_inferences":["A clean way to test whether symbolic derivation is a cause rather than a correlate would be to prompt a numeric-first model such as Deepseek-V3 to derive a general expression before substituting numbers; if its accuracy rises toward the reasoning models, the style itself carries part of the advantage.","The same symbolic-versus-numeric distinction could plausibly extend to other quantitative STEM tasks, such as circuit analysis, stoichiometry, or geometric optics, where multi-step problems reward holding variables symbolically until the final substitution.","For model developers, the results suggest that training rewards for symbolic algebraic rearrangement, rather than numeric computation, could improve correctness on textbook physics, a hypothesis the paper does not directly test."],"forward_implications":["Reasoning-specialized open-weight models can handle a substantial fraction of filtered textbook physics problems without external tools, retrieval, or task-specific prompt engineering.","Few-shot chain-of-thought prompting still provides measurable gains on top of strong zero-shot reasoning, with classical dynamics improving from 62.5% to 84.8% and overall from 75.9% to 81.3%.","For multi-step physics problems, a model's reasoning style, symbolic derivation versus immediate numeric substitution, is associated with correctness, meaning reasoning quality and answer accuracy are not separable in these models.","Distillation can preserve or even improve physics reasoning at smaller scale, since R1-distill-Qwen-32B beats R1-distill-LLaMA-70B on fundamental physics and thermodynamics, so parameter count is not the only driver of performance.","The average token counts reported in Table 3 imply that the accuracy gains come with high inference latency and memory load, which limits deployment in low-resource settings."],"supporting_citations":[{"why":"Supplies the SciBench physics datasets, the baseline accuracy numbers for chat models, and the error-categorization framework used throughout the comparison.","marker":"(Chen et al., 2023)"},{"why":"Provides the chain-of-thought prompting method that defines the zero-shot and few-shot prompting conditions.","marker":"(Wei et al., 2022)"},{"why":"Establishes few-shot exemplar prompting, the basis for the few-shot chain-of-thought condition and the choice of three exemplars.","marker":"(Brown et al., 2020)"},{"why":"Establishes zero-shot chain-of-thought reasoning, the baseline prompting strategy used to elicit step-by-step answers.","marker":"(Kojima et al., 2022)"},{"why":"Describes instruction tuning with human feedback, the training paradigm that distinguishes reasoning-specialized models from chat models.","marker":"(Ouyang et al., 2022)"},{"why":"Provides GPT-4 and GPT-4-Turbo as the strongest chat-model baselines whose SciBench scores are compared against Deepseek-R1.","marker":"(OpenAI, 2023)"}],"fun_headline_variants":["Physics reasoning: symbolic derivation beats numeric crunching","Why Deepseek-R1 wins at physics: it thinks symbolically","Symbolic first, numbers later: LLM physics strategy","Deepseek-R1's physics edge: symbol manipulation over arithmetic","Reasoning LLMs solve physics by symbolic derivation, not numeric steps"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper's behavioral conclusion rests on the assumption that the classifier it uses can reliably sort the model's step-by-step written reasoning into the six named patterns, and that the symbolic-versus-numeric distinction is clean enough to be labeled consistently.","fun_headline_variants_meta":{"raw":{"variants":["Physics reasoning: symbolic derivation beats numeric crunching","Why Deepseek-R1 wins at physics: it thinks symbolically","Symbolic first, numbers later: LLM physics strategy","Deepseek-R1's physics edge: symbol manipulation over arithmetic","Reasoning LLMs solve physics by symbolic derivation, not numeric steps"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000141,"raw_usage":{"total_tokens":1108,"prompt_tokens":836,"completion_tokens":272,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":452,"completion_tokens_details":{"reasoning_tokens":188}},"tokens_in":452,"tokens_out":272,"duration_ms":3465,"temperature":1.0,"reasoning_tokens":188,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T20:53:57.370567+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the pattern analysis on the same 193 problems with two independent human annotators labeling each reasoning trace as symbolic or numeric, and compare their labels with the classifier's; if agreement is near chance, or if rewording the classifier's prompt changes the labels, the reported link between symbolic derivation and correctness would not survive.","supporting_citations":[{"cited_title":"Le, and Denny Zhou","cited_arxiv_id":null,"evidence_quote":"Provides the chain-of-thought prompting method that defines the zero-shot and few-shot prompting conditions."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Establishes zero-shot chain-of-thought reasoning, the baseline prompting strategy used to elicit step-by-step answers."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Describes instruction tuning with human feedback, the training paradigm that distinguishes reasoning-specialized models from chat models."}],"review_version":1}